OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems
Venue: arXiv cs.LG 2020
Year: 2020

Convolutional sparse representation (CSR), shift-invariant model for inverse problems, has gained much attention in the fields of signal/image processing, machine learning and computer vision. The most challenging problems in CSR implies the minimization of a composite function of the form $min_x \sum_i f_i(x) + g(x)$, where a direct and low-cost solution can be difficult to achieve. However, it has been reported that semi-distributed formulations such as ADMM consensus can provide important computational benefits. In the present work, we derive and detail a thorough theoretical analysis of an efficient consensus algorithm based on proximal gradient (PG) approach. The effectiveness of the proposed algorithm with respect to its ADMM counterpart is primarily assessed in the classic convolutional dictionary learning problem. Furthermore, our consensus method, which is generically structured, can be used to solve other optimization problems, where a sum of convex functions with a regularization term share a single global variable. As an example, the proposed algorithm is also applied to another particular convolutional problem for the anomaly detection task.
Sentiment Classification in Bangla Textual Content: A Comparative Study
Venue: arXiv cs.CL 2020
Year: 2020

Sentiment analysis has been widely used to understand our views on social and political agendas or user experiences over a product. It is one of the cores and well-researched areas in NLP. However, for low-resource languages, like Bangla, one of the prominent challenge is the lack of resources. Another important limitation, in the current literature for Bangla, is the absence of comparable results due to the lack of a well-defined train/test split. In this study, we explore several publicly available sentiment labeled datasets and designed classifiers using both classical and deep learning algorithms. In our study, the classical algorithms include SVM and Random Forest, and deep learning algorithms include CNN, FastText, and transformer-based models. We compare these models in terms of model performance and time-resource complexity. Our finding suggests transformer-based models, which have not been explored earlier for Bangla, outperform all other models. Furthermore, we created a weighted list of lexicon content based on the valence score per class. We then analyzed the content for high significance entries per class, in the datasets. For reproducibility, we make publicly available data splits and the ranked lexicon list. The presented results can be used for future studies as a benchmark.
Escherization with Generalized Distance Functions Focusing on Local Structural Similarity
Venue: arXiv cs.CG 2020
Year: 2020

The Escherization problem involves finding a closed figure that tiles the plane that is most similar to a given goal figure. In Koizumi and Sugihara's formulation of the Escherization problem, the tile and goal figures are represented as $n$-point polygons where the similarity between them is measured based on the difference in the positions between the corresponding points. This paper presents alternative similarity measures (distance functions) suitable for this problem. The proposed distance functions focus on the similarity of local structures in several different manners. The designed distance functions are incorporated into a recently developed framework of the exhaustive search of the templates for the Escherization problem. Efficient exhaustive and incomplete search algorithms for the formulated problems are also developed to obtain results within a reasonable computation time. Experimental results showed that the proposed algorithms found satisfactory tile shapes for fairly complicated goal figures in a reasonable computation time.
Action Duration Prediction for Segment-Level Alignment of Weakly-Labeled Videos
Venue: arXiv cs.CV 2020
Year: 2020

This paper focuses on weakly-supervised action alignment, where only the ordered sequence of video-level actions is available for training. We propose a novel Duration Network, which captures a short temporal window of the video and learns to predict the remaining duration of a given action at any point in time with a level of granularity based on the type of that action. Further, we introduce a Segment-Level Beam Search to obtain the best alignment, that maximizes our posterior probability. Segment-Level Beam Search efficiently aligns actions by considering only a selected set of frames that have more confident predictions. The experimental results show that our alignments for long videos are more robust than existing models. Moreover, the proposed method achieves state of the art results in certain cases on the popular Breakfast and Hollywood Extended datasets.
A lightweight cryptography (LWC) framework to secure memory heap in Internet of Things
Venue: arXiv cs.CR 2020
Year: 2020

The extensive networking of devices and the large amount of data generated from the Internet of Things (IoT) has brought security issues to the attention of the researcher. Java is the most common platform for embedded applications such as IoT, Wireless Sensors Networks (WSN), Near Field Communications (NFC) and Radio Frequency Identification (RFID). The object programming languages such as Java, SWIFT, PHP and C++ use garbage collection after any object run which creates security loophole for attacks such as Next Memory Address Occupation (NMAO), memory replay, Learning Tasks Behaviors (LTB). The security risk increases in IoT when attacks exceeds the target device to the surrounding connected devices. Inappropriate or wrong operations causes energy loss and increased costs. In this paper, a security method to protect IoT system operation from memory heap penetration and address modification attack is proposed. The proposed method prevents directed attack by encrypting the object Garbage Collection at run time. To form a unique signature mechanism, the Cryptographic Hash Function (CHF) which employs a specific one-way hash algorithm. The proposed framework uses L-function based ECC and one-time Key (OTK) to secure the memory heap. Our method is used with open system where the effect on the operating system is not considered. The proposed method proved to be powerful and efficient which can help in achieving higher levels of security across several IoT applications, by enabling better detection of malicious attacks.
How do you feel: Emotions exhibited while Playing Computer Games and their Relationship with Gaming Behaviors
Venue: arXiv cs.CY 2020
Year: 2020

This descriptive study utilized a validated questionnaire to determine the emotions exhibited by computer gamers in cyber cafés. It was revealed that most of the gamers were young, male, single, as well as high school and vocational students who belonged to middle-income families. Most of them had computer access at home but only a few had Internet access at home. Gamers tended to play games in cyber cafés at least three times a week, usually in the evening, for at least two hours per visit. They also reported that they played games frequently. Majority of the gamers were fond of playing DOTA, League of Legends, and CABAL and they had been playing games for at least two years. It was disclosed that they exhibited both positive and negative emotions while playing games. It was shown that gamers were inclined to be more anxious to be defeated in a game as gaming became frequent and length of years in playing games increased. They also had the tendency to become more stressed when length of years of playing games increased. On the other hand, other gaming behaviors were not significantly related to other emotions. Thus, the null hypothesis stating that gaming behaviors of the respondents are not significantly related to the emotions exhibited while playing the computer games is partially rejected. Therefore, not all emotions exhibited while playing computer games could be attributed to their gaming behaviors. It is recommended that other emotions such as anger, frustration, boredom, amusement, etc. be included in future research.
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches
Venue: arXiv cs.CV 2020
Year: 2020

Non-local self-similarity based low rank algorithms are the state-of-the-art methods for image denoising. In this paper, a new method is proposed by solving two issues: how to improve similar patches matching accuracy and build an appropriate low rank matrix approximation model for Gaussian noise. For the first issue, similar patches can be found locally or globally. Local patch matching is to find similar patches in a large neighborhood which can alleviate noise effect, but the number of patches may be insufficient. Global patch matching is to determine enough similar patches but the error rate of patch matching may be higher. Based on this, we first use local patch matching method to reduce noise and then use Gaussian patch mixture model to achieve global patch matching. The second issue is that there is no low rank matrix approximation model to adapt to Gaussian noise. We build a new model according to the characteristics of Gaussian noise, then prove that there is a globally optimal solution of the model. By solving the two issues, experimental results are reported to show that the proposed approach outperforms the state-of-the-art denoising methods includes several deep learning ones in both PSNR / SSIM values and visual quality.
Simulation-based Testing for Early Safety-Validation of Robot Systems
Venue: arXiv cs.RO 2020
Year: 2020

Industrial human-robot collaborative systems must be validated thoroughly with regard to safety. The sooner potential hazards for workers can be exposed, the less costly is the implementation of necessary changes. Due to the complexity of robot systems, safety flaws often stay hidden, especially at early design stages, when a physical implementation is not yet available for testing. Simulation-based testing is a possible way to identify hazards in an early stage. However, creating simulation conditions in which hazards become observable can be difficult. Brute-force or Monte-Carlo-approaches are often not viable for hazard identification, due to large search spaces. This work addresses this problem by using a human model and an optimization algorithm to generate high-risk human behavior in simulation, thereby exposing potential hazards. A proof of concept is shown in an application example where the method is used to find hazards in an industrial robot cell.
Experiences from Large-Scale Model Checking: Verification of a Vehicle Control System
Venue: arXiv cs.SE 2020
Year: 2020

In the age of autonomously driving vehicles, functionality and complexity of embedded systems are increasing tremendously. Safety aspects become more important and require such systems to operate with the highest possible level of fault tolerance. Simulation and systematic testing techniques have reached their limits in this regard. Here, formal verification as a long established technique can be an appropriate complement. However, the necessary preparatory work like adequately modeling a system and specifying properties in temporal logic are anything but trivial. In this paper, we report on our experiences applying model checking to verify the arbitration logic of a Vehicle Control System. We balance pros and cons of different model checking techniques and tools, and reason about our choice of the symbolic model checker NuSMV. We describe the process of modeling the architecture, resulting in ~1500 LOC, 69 state variables and 38 LTL constraints. To handle this large-scale model, we automate and optimize the model checking procedure for use on multi-core CPUs and employ Bounded Model Checking to avoid the state explosion problem. We share our lessons learned and provide valuable insights for architects, developers, and test engineers involved in this highly present topic.
A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis
Venue: arXiv cs.CL 2020
Year: 2020

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread all over the world, resulting in a deadly pandemic that infected millions of people around the globe. The government and public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the social and economic lives of the people. In this paper, we extract and study the opinion of people from the top five worst affected countries by the virus, namely USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanism to extract the positive, negative, and neutral sentiments. The embedding layer encodes the sentence sequence into a real-valued vector. The word-level and sentence-level encoding is performed by a 1D Conv-BiGRU based mechanism, followed by word-level and sentence-level attention, respectively. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter. Extensive experiments on our proposed dataset demonstrate the effectiveness of the proposed MACBiG-Net. Also, attention-weights visualization and in-depth results analysis shows that the proposed network has effectively captured the sentiments of the people.
Fixed-Parameter Algorithms for Graph Constraint Logic
Venue: arXiv cs.DS 2020
Year: 2020

Non-deterministic constraint logic (NCL) is a simple model of computation based on orientations of a constraint graph with edge weights and vertex demands. NCL captures \PSPACE\xspace and has been a useful tool for proving algorithmic hardness of many puzzles, games, and reconfiguration problems. In particular, its usefulness stems from the fact that it remains \PSPACE-complete even under severe restrictions of the weights (e.g., only edge-weights one and two are needed) and the structure of the constraint graph (e.g., planar \textsc{and/or}\xspace graphs of bounded bandwidth). While such restrictions on the structure of constraint graphs do not seem to limit the expressiveness of NCL, the building blocks of the constraint graphs cannot be limited without losing expressiveness: We consider as parameters the number of weight-one edges and the number of weight-two edges of a constraint graph, as well as the number of \textsc{and}\xspace or \textsc{or}\xspace vertices of an \textsc{and/or}\xspace constraint graph. We show that NCL is fixed-parameter tractable (FPT) for any of these parameters. In particular, for NCL parameterized by the number of weight-one edges or the number of \textsc{and}\xspace vertices, we obtain a linear kernel. It follows that, in a sense, NCL as introduced by Hearn and Demaine is defined in the most economical way for the purpose of capturing \PSPACE.
Topic modelling discourse dynamics in historical newspapers
Venue: arXiv cs.CL 2020
Year: 2020

This paper addresses methodological issues in diachronic data analysis for historical research. We apply two families of topic models (LDA and DTM) on a relatively large set of historical newspapers, with the aim of capturing and understanding discourse dynamics. Our case study focuses on newspapers and periodicals published in Finland between 1854 and 1917, but our method can easily be transposed to any diachronic data. Our main contributions are a) a combined sampling, training and inference procedure for applying topic models to huge and imbalanced diachronic text collections; b) a discussion on the differences between two topic models for this type of data; c) quantifying topic prominence for a period and thus a generalization of document-wise topic assignment to a discourse level; and d) a discussion of the role of humanistic interpretation with regard to analysing discourse dynamics through topic models.
Planning Folding Motion with Simulation in the Loop Using Laser Forming Origami and Thermal Behaviors as an Example
Venue: arXiv cs.RO 2020
Year: 2020

Designing a robot or structure that can fold itself into a target shape is a process that involves challenges originated from multiple sources. For example, the designer of rigid self-folding robots must consider foldability from geometric and kinematic aspects to avoid self-intersection and undesired deformations. Recent works have shown success in estimating foldability of a design using robot motion planners. However, many foldable structures are actuated using physically coupled reactions (i.e., folding originated from thermal, chemical, or electromagnetic loads). Therefore, a reliable foldability analysis must consider additional constraints that resulted from these critical phenomena. This work investigates the idea of efficiently incorporating computationally expensive physics simulation within the folding motion planner to provide a better estimation of the foldability. In this paper, we will use laser forming origami as an example to demonstrate the benefits of considering the properties beyond geometry. We show that the design produced by the proposed method can be folded more efficiently.
Space Lower Bounds for Graph Stream Problems
Venue: arXiv cs.CC 2020
Year: 2020

This work concerns with proving space lower bounds for graph problems in the streaming model. It is known that computing the length of shortest path between two nodes in the streaming model requires $Ω(n)$ space, where $n$ is the number of nodes in the graph. We study the problem of finding the depth of a given node in a rooted tree in the streaming model. For this problem we prove a tight single pass space lower bound and a multipass space lower bound. As this is a special case of computing shortest paths on graphs, the above lower bounds also apply to the shortest path problem in the streaming model. The results are obtained by using known communication complexity lower bounds or by constructing hard instances for the problem. Additionally, we apply the techniques used in proving the above lower bound results to prove space lower bounds (single and multipass) for other graph problems like finding min $s-t$ cut, detecting negative weight cycle and finding whether two nodes lie in the same strongly connected component.
Graph Signal Recovery Using Restricted Boltzmann Machines
Venue: arXiv cs.LG 2020
Year: 2020

We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network. The proposed pipeline requires the deep neural network that is trained on a downward machine learning task with clean data, data which is free from any form of corruption or incompletion. We show that denoising the representations learned by the deep neural networks is usually more effective than denoising the data itself. Although this pipeline can deal with noise in any dataset, it is particularly effective for graph-structured datasets.
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility
Venue: arXiv cs.NI 2020
Year: 2020

Bandwidth forecasting in Mobile Broadband (MBB) networks is a challenging task, particularly when coupled with a degree of mobility. In this work, we introduce HINDSIGHT++, an open-source R-based framework for bandwidth forecasting experimentation in MBB networks with Long Short Term Memory (LSTM) networks. We instrument HINDSIGHT++ following an Automated Machine Learning (AutoML) paradigm to first, alleviate the burden of data preprocessing, and second, enhance performance related aspects. We primarily focus on bandwidth forecasting for Fifth Generation (5G) networks. In particular, we leverage 5Gophers, the first open-source attempt to measure network performance on operational 5G networks in the US. We further explore the LSTM performance boundaries on Fourth Generation (4G) commercial settings using NYU-METS, an open-source dataset comprising of hundreds of bandwidth traces spanning different mobility scenarios. Our study aims to investigate the impact of hyperparameter optimization on achieving state-of-the-art performance and beyond. Results highlight its significance under 5G scenarios showing an average Mean Absolute Error (MAE) decrease of near 30% when compared to prior state-of-the-art values. Due to its universal design, we argue that HINDSIGHT++ can serve as a handy software tool for a multitude of applications in other scientific fields.
Scalable Local Timestepping on Octree Grids
Venue: arXiv cs.MS 2020
Year: 2020

Numerical solutions of hyperbolic partial differential equations(PDEs) are ubiquitous in science and engineering. Method of lines is a popular approach to discretize PDEs defined in spacetime, where space and time are discretized independently. When using explicit timesteppers on adaptive grids, the use of a global timestep-size dictated by the finest grid-spacing leads to inefficiencies in the coarser regions. Even though adaptive space discretizations are widely used in computational sciences, temporal adaptivity is less common due to its sophisticated nature. In this paper, we present highly scalable algorithms to enable local timestepping (LTS) for explicit timestepping schemes on fully adaptive octrees. We demonstrate the accuracy of our methods as well as the scalability of our framework across 16K cores in TACC's Frontera. We also present a speed up estimation model for LTS, which predicts the speedup compared to global timestepping (GTS) with an average of 0.1 relative error.
Study of Resource Amount Configuration for Automatic Application Offloading
Venue: arXiv cs.DC 2020
Year: 2020

In recent years, utilization of heterogeneous hardware other than small core CPU such as GPU, FPGA or many core CPU is increasing. However, when using heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and OpenCL are high. Based on that, I have proposed environment-adaptive software that enables automatic conversion, configuration, and high performance operation of once written code, according to the hardware to be placed. However, although the conversion of the code according to the migration destination environment has been studied so far, there has been no research to properly set the resource amount. In this paper, as a new element of environment adaptive software, in order to operate the application with high cost performance, I study a method to optimize the resource amount of CPUs and offload devices.
What do we expect from Multiple-choice QA Systems?
Venue: arXiv cs.CL 2020
Year: 2020

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human's expectations from models that "understand" language. In this work we consider a top performing model on several Multiple Choice Question Answering (MCQA) datasets, and evaluate it against a set of expectations one might have from such a model, using a series of zero-information perturbations of the model's inputs. Our results show that the model clearly falls short of our expectations, and motivates a modified training approach that forces the model to better attend to the inputs. We show that the new training paradigm leads to a model that performs on par with the original model while better satisfying our expectations.